Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jonas/bert-base-uncased-finetuned-sdg-Mar23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonas/bert-base-uncased-finetuned-sdg-Mar23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonas/bert-base-uncased-finetuned-sdg-Mar23")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonas/bert-base-uncased-finetuned-sdg-Mar23") model = AutoModelForSequenceClassification.from_pretrained("jonas/bert-base-uncased-finetuned-sdg-Mar23", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jonas/bert-base-uncased-finetuned-sdg-Mar23: direct link, hf CLI and curl.
- Browser
- Download file 3.57 kB
-
https://huggingface.co/jonas/bert-base-uncased-finetuned-sdg-Mar23/resolve/main/training_args.bin
- Command line
-
hf download hf://jonas/bert-base-uncased-finetuned-sdg-Mar23/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jonas/bert-base-uncased-finetuned-sdg-Mar23/resolve/main/training_args.bin
3.57 kB
- Xet hash:
- 94e9c2266d386b2270375092f9bfafbcc3fcc79520354852b80ed8b1d1b5a851
- Size of remote file:
- 3.57 kB
- SHA256:
- 3f4399fb8ba3517abbd7753aa83675cdf1b885ffe6aa4fff8010d09928b954a0
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